{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9305-CDSKC</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1452-KIOVK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>89.10</td>\n",
       "      <td>1949.4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6713-OKOMC</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>29.75</td>\n",
       "      <td>301.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7892-POOKP</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6388-TABGU</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>62</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>56.15</td>\n",
       "      <td>3487.95</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "5  9305-CDSKC  Female              0      No         No       8          Yes   \n",
       "6  1452-KIOVK    Male              0      No        Yes      22          Yes   \n",
       "7  6713-OKOMC  Female              0      No         No      10           No   \n",
       "8  7892-POOKP  Female              0     Yes         No      28          Yes   \n",
       "9  6388-TABGU    Male              0      No        Yes      62          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "5               Yes     Fiber optic             No           No   \n",
       "6               Yes     Fiber optic             No          Yes   \n",
       "7  No phone service             DSL            Yes           No   \n",
       "8               Yes     Fiber optic             No           No   \n",
       "9                No             DSL            Yes          Yes   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "5              Yes          No         Yes             Yes  Month-to-month   \n",
       "6               No          No         Yes              No  Month-to-month   \n",
       "7               No          No          No              No  Month-to-month   \n",
       "8              Yes         Yes         Yes             Yes  Month-to-month   \n",
       "9               No          No          No              No        One year   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "5              Yes           Electronic check           99.65        820.5   \n",
       "6              Yes    Credit card (automatic)           89.10       1949.4   \n",
       "7               No               Mailed check           29.75        301.9   \n",
       "8              Yes           Electronic check          104.80      3046.05   \n",
       "9               No  Bank transfer (automatic)           56.15      3487.95   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  \n",
       "5   Yes  \n",
       "6    No  \n",
       "7    No  \n",
       "8   Yes  \n",
       "9    No  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Telco-Customer-Churn.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
       "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 人工数据特征分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#基本信息，开通业务，签订合约"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7043"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate check\n",
    "data['customerID'].drop_duplicates().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          False\n",
       "gender              False\n",
       "SeniorCitizen       False\n",
       "Partner             False\n",
       "Dependents          False\n",
       "tenure              False\n",
       "PhoneService        False\n",
       "MultipleLines       False\n",
       "InternetService     False\n",
       "OnlineSecurity      False\n",
       "OnlineBackup        False\n",
       "DeviceProtection    False\n",
       "TechSupport         False\n",
       "StreamingTV         False\n",
       "StreamingMovies     False\n",
       "Contract            False\n",
       "PaperlessBilling    False\n",
       "PaymentMethod       False\n",
       "MonthlyCharges      False\n",
       "TotalCharges        False\n",
       "Churn               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill nan\n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4472-LVYGI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>3115-CZMZD</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.25</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>5709-LVOEQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>80.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>4367-NUYAO</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.75</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>1371-DWPAZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>56.05</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>7644-OMVMY</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>3213-VVOLG</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</th>\n",
       "      <td>2520-SGTTA</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.00</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>2923-ARZLG</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.70</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>4075-WKNIU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>73.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>2775-SEFEE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "488   4472-LVYGI  Female              0     Yes        Yes       0   \n",
       "753   3115-CZMZD    Male              0      No        Yes       0   \n",
       "936   5709-LVOEQ  Female              0     Yes        Yes       0   \n",
       "1082  4367-NUYAO    Male              0     Yes        Yes       0   \n",
       "1340  1371-DWPAZ  Female              0     Yes        Yes       0   \n",
       "3331  7644-OMVMY    Male              0     Yes        Yes       0   \n",
       "3826  3213-VVOLG    Male              0     Yes        Yes       0   \n",
       "4380  2520-SGTTA  Female              0     Yes        Yes       0   \n",
       "5218  2923-ARZLG    Male              0     Yes        Yes       0   \n",
       "6670  4075-WKNIU  Female              0     Yes        Yes       0   \n",
       "6754  2775-SEFEE    Male              0      No        Yes       0   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService       OnlineSecurity  \\\n",
       "488            No  No phone service             DSL                  Yes   \n",
       "753           Yes                No              No  No internet service   \n",
       "936           Yes                No             DSL                  Yes   \n",
       "1082          Yes               Yes              No  No internet service   \n",
       "1340           No  No phone service             DSL                  Yes   \n",
       "3331          Yes                No              No  No internet service   \n",
       "3826          Yes               Yes              No  No internet service   \n",
       "4380          Yes                No              No  No internet service   \n",
       "5218          Yes                No              No  No internet service   \n",
       "6670          Yes               Yes             DSL                   No   \n",
       "6754          Yes               Yes             DSL                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "488                    No                  Yes                  Yes   \n",
       "753   No internet service  No internet service  No internet service   \n",
       "936                   Yes                  Yes                   No   \n",
       "1082  No internet service  No internet service  No internet service   \n",
       "1340                  Yes                  Yes                  Yes   \n",
       "3331  No internet service  No internet service  No internet service   \n",
       "3826  No internet service  No internet service  No internet service   \n",
       "4380  No internet service  No internet service  No internet service   \n",
       "5218  No internet service  No internet service  No internet service   \n",
       "6670                  Yes                  Yes                  Yes   \n",
       "6754                  Yes                   No                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "488                   Yes                   No  Two year              Yes   \n",
       "753   No internet service  No internet service  Two year               No   \n",
       "936                   Yes                  Yes  Two year               No   \n",
       "1082  No internet service  No internet service  Two year               No   \n",
       "1340                  Yes                   No  Two year               No   \n",
       "3331  No internet service  No internet service  Two year               No   \n",
       "3826  No internet service  No internet service  Two year               No   \n",
       "4380  No internet service  No internet service  Two year               No   \n",
       "5218  No internet service  No internet service  One year              Yes   \n",
       "6670                  Yes                   No  Two year               No   \n",
       "6754                   No                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "488   Bank transfer (automatic)           52.55                 No  \n",
       "753                Mailed check           20.25                 No  \n",
       "936                Mailed check           80.85                 No  \n",
       "1082               Mailed check           25.75                 No  \n",
       "1340    Credit card (automatic)           56.05                 No  \n",
       "3331               Mailed check           19.85                 No  \n",
       "3826               Mailed check           25.35                 No  \n",
       "4380               Mailed check           20.00                 No  \n",
       "5218               Mailed check           19.70                 No  \n",
       "6670               Mailed check           73.35                 No  \n",
       "6754  Bank transfer (automatic)           61.90                 No  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "data[data['TotalCharges'] == ' ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['TotalCharges'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['SeniorCitizen'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "此时TotalCharges是否已经转换为浮点型： True\n",
      "此时TotalCharges存在11行缺失样本。\n"
     ]
    }
   ],
   "source": [
    "#  convert_numeric如果为True，则尝试强制转换为数字，不可转换的变为NaN\n",
    "data['TotalCharges'] = data['TotalCharges'].apply(pd.to_numeric, errors='coerce') \n",
    "\n",
    "print(\"此时TotalCharges是否已经转换为浮点型：\", data['TotalCharges'].dtype == 'float')\n",
    "print(\"此时TotalCharges存在%s行缺失样本。\" % data['TotalCharges'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnDf = data['TotalCharges'].fillna(0).to_frame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data\n",
    "df['TotalCharges'] = fnDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>0</td>\n",
       "      <td>52.55</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0</td>\n",
       "      <td>20.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
       "      <td>80.85</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>0</td>\n",
       "      <td>25.75</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>0</td>\n",
       "      <td>56.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>0</td>\n",
       "      <td>19.85</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>0</td>\n",
       "      <td>25.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</th>\n",
       "      <td>0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>0</td>\n",
       "      <td>19.70</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>0</td>\n",
       "      <td>73.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>0</td>\n",
       "      <td>61.90</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tenure  MonthlyCharges  TotalCharges\n",
       "488        0           52.55           0.0\n",
       "753        0           20.25           0.0\n",
       "936        0           80.85           0.0\n",
       "1082       0           25.75           0.0\n",
       "1340       0           56.05           0.0\n",
       "3331       0           19.85           0.0\n",
       "3826       0           25.35           0.0\n",
       "4380       0           20.00           0.0\n",
       "5218       0           19.70           0.0\n",
       "6670       0           73.35           0.0\n",
       "6754       0           61.90           0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data[data['TotalCharges'] == 0].filter(['tenure','MonthlyCharges','TotalCharges'])\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 这些就是当月流失的客户 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['TotalCharges'] = data['TotalCharges'].fillna(data['MonthlyCharges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>39.65</td>\n",
       "      <td>39.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1066-JKSGK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8665-UTDHZ</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>30.20</td>\n",
       "      <td>30.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7310-EGVHZ</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>20.20</td>\n",
       "      <td>20.20</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6979</th>\n",
       "      <td>5351-QESIO</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>24.20</td>\n",
       "      <td>24.20</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7010</th>\n",
       "      <td>0723-DRCLG</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>74.45</td>\n",
       "      <td>74.45</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7016</th>\n",
       "      <td>1471-GIQKQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>49.95</td>\n",
       "      <td>49.95</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7018</th>\n",
       "      <td>1122-JWTJW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>70.65</td>\n",
       "      <td>70.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7032</th>\n",
       "      <td>6894-LFHLY</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>75.75</td>\n",
       "      <td>75.75</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "20    8779-QRDMV    Male              1      No         No       1   \n",
       "22    1066-JKSGK    Male              0      No         No       1   \n",
       "27    8665-UTDHZ    Male              0     Yes        Yes       1   \n",
       "33    7310-EGVHZ    Male              0      No         No       1   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "6979  5351-QESIO    Male              0      No        Yes       1   \n",
       "7010  0723-DRCLG  Female              1     Yes         No       1   \n",
       "7016  1471-GIQKQ  Female              0      No         No       1   \n",
       "7018  1122-JWTJW    Male              0     Yes        Yes       1   \n",
       "7032  6894-LFHLY    Male              1      No         No       1   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService       OnlineSecurity  \\\n",
       "0              No  No phone service             DSL                   No   \n",
       "20             No  No phone service             DSL                   No   \n",
       "22            Yes                No              No  No internet service   \n",
       "27             No  No phone service             DSL                   No   \n",
       "33            Yes                No              No  No internet service   \n",
       "...           ...               ...             ...                  ...   \n",
       "6979           No  No phone service             DSL                   No   \n",
       "7010          Yes               Yes     Fiber optic                   No   \n",
       "7016          Yes                No             DSL                   No   \n",
       "7018          Yes                No     Fiber optic                   No   \n",
       "7032          Yes               Yes     Fiber optic                   No   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "0                     Yes                   No                   No   \n",
       "20                     No                  Yes                   No   \n",
       "22    No internet service  No internet service  No internet service   \n",
       "27                    Yes                   No                   No   \n",
       "33    No internet service  No internet service  No internet service   \n",
       "...                   ...                  ...                  ...   \n",
       "6979                   No                   No                   No   \n",
       "7010                   No                   No                   No   \n",
       "7016                  Yes                   No                   No   \n",
       "7018                   No                   No                   No   \n",
       "7032                   No                   No                   No   \n",
       "\n",
       "              StreamingTV      StreamingMovies        Contract  \\\n",
       "0                      No                   No  Month-to-month   \n",
       "20                     No                  Yes  Month-to-month   \n",
       "22    No internet service  No internet service  Month-to-month   \n",
       "27                     No                   No  Month-to-month   \n",
       "33    No internet service  No internet service  Month-to-month   \n",
       "...                   ...                  ...             ...   \n",
       "6979                   No                   No  Month-to-month   \n",
       "7010                   No                   No  Month-to-month   \n",
       "7016                   No                   No  Month-to-month   \n",
       "7018                   No                   No  Month-to-month   \n",
       "7032                   No                   No  Month-to-month   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
       "0                 Yes           Electronic check           29.85   \n",
       "20                Yes           Electronic check           39.65   \n",
       "22                 No               Mailed check           20.15   \n",
       "27                 No           Electronic check           30.20   \n",
       "33                 No  Bank transfer (automatic)           20.20   \n",
       "...               ...                        ...             ...   \n",
       "6979               No               Mailed check           24.20   \n",
       "7010              Yes           Electronic check           74.45   \n",
       "7016               No           Electronic check           49.95   \n",
       "7018              Yes               Mailed check           70.65   \n",
       "7032              Yes           Electronic check           75.75   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "0            29.85    No  \n",
       "20           39.65   Yes  \n",
       "22           20.15   Yes  \n",
       "27           30.20   Yes  \n",
       "33           20.20    No  \n",
       "...            ...   ...  \n",
       "6979         24.20    No  \n",
       "7010         74.45   Yes  \n",
       "7016         49.95    No  \n",
       "7018         70.65   Yes  \n",
       "7032         75.75   Yes  \n",
       "\n",
       "[624 rows x 21 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['TotalCharges'] == data['MonthlyCharges']] #只用了一个月的人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>52.55</td>\n",
       "      <td>52.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>20.25</td>\n",
       "      <td>20.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>80.85</td>\n",
       "      <td>80.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>25.75</td>\n",
       "      <td>25.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>56.05</td>\n",
       "      <td>56.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>19.85</td>\n",
       "      <td>19.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>25.35</td>\n",
       "      <td>25.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</th>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>19.70</td>\n",
       "      <td>19.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>73.35</td>\n",
       "      <td>73.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>61.90</td>\n",
       "      <td>61.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalCharges  MonthlyCharges\n",
       "488          52.55           52.55\n",
       "753          20.25           20.25\n",
       "936          80.85           80.85\n",
       "1082         25.75           25.75\n",
       "1340         56.05           56.05\n",
       "3331         19.85           19.85\n",
       "3826         25.35           25.35\n",
       "4380         20.00           20.00\n",
       "5218         19.70           19.70\n",
       "6670         73.35           73.35\n",
       "6754         61.90           61.90"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['tenure'] == 0][['TotalCharges', 'MonthlyCharges']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7043.000000\n",
       "mean        0.162147\n",
       "std         0.368612\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max         1.000000\n",
       "Name: SeniorCitizen, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SeniorCitizen'].describe() #也是类别数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 箱型图观察异常值情况\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt    # 可视化\n",
    "# 在Jupyter notebook里嵌入图片\n",
    "%matplotlib inline\n",
    "\n",
    "# 分析百分比特征\n",
    "fig = plt.figure(figsize=(15,6)) # 建立图像\n",
    "\n",
    "# tenure特征\n",
    "ax1 = fig.add_subplot(311)    # 子图1\n",
    "list1 = list(data['tenure'])\n",
    "ax1.boxplot(list1, vert=False, showmeans=True, flierprops = {\"marker\":\"o\",\"markerfacecolor\":\"steelblue\"})\n",
    "ax1.set_title('tenure')\n",
    "\n",
    "# MonthlyCharges特征\n",
    "ax2 = fig.add_subplot(312)    # 子图2\n",
    "list2 = list(data['MonthlyCharges'])\n",
    "ax2.boxplot(list2, vert=False, showmeans=True, flierprops = {\"marker\":\"o\",\"markerfacecolor\":\"steelblue\"})\n",
    "ax2.set_title('MonthlyCharges')\n",
    "\n",
    "# TotalCharges\n",
    "ax3 = fig.add_subplot(313)    # 子图3\n",
    "list3 = list(data['TotalCharges'])\n",
    "ax3.boxplot(list3, vert=False, showmeans=True, flierprops = {\"marker\":\"o\",\"markerfacecolor\":\"steelblue\"})\n",
    "ax3.set_title('TotalCharges')\n",
    "\n",
    "plt.tight_layout(pad=1.5)    # 设置子图之间的间距\n",
    "plt.show() # 展示箱型图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无需异常处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察是否存在类别不平衡现象\n",
    "p = data['Churn'].value_counts()    # 目标变量正负样本的分布\n",
    "\n",
    "plt.figure(figsize=(10,6))    \n",
    "\n",
    "\n",
    "patches, l_text, p_text = plt.pie(p,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "# l_text是饼图对着文字大小，p_text是饼图内文字大小\n",
    "for t in p_text:\n",
    "    t.set_size(15)\n",
    "for t in l_text:\n",
    "    t.set_size(15)\n",
    "plt.title('Total Churn')\n",
    "plt.savefig('total_churn.jpg')\n",
    "plt.show()    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mViHlnL0j4E5gfUTcXLTqUWBaWp4GPFJUnyKpl6ShFD6wXZ2mgHZIOj1tc2pRHzMzq4ByLs46A/g28JKkF1PtvwALgKWSLgeagIsAImKdpKXAKxTO/JkZEa2p3wzgHqAP8GR6mJlZhbQb+hGxktLz8QDjD9JnPjC/RL0RGHUoAzQzs47jK3LNzDLi0Dczy4hD38wsI77LplkXV7frgWoPoUvZVO0BHCEf6ZuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZaTd0Jd0l6Stkl4uqvWXtFzS79PzcUXrrpW0QdJrkiYU1cdIeimtWyhJHf92zMysLeUc6d8DTNyvNhdYERHDgBXpNZJGAFOAkanPTyR1T31uA6YDw9Jj/22amVknazf0I+JZ4F/2K08GFqflxcD5RfUlEbE7Il4HNgBjJR0P9IuIVRERwL1FfczMrEIOd05/cES8BZCeB6X6EODNonbNqTYkLe9fL0nSdEmNkhq3bdt2mEM0M7P9dfQHuaXm6aONekkRsSgi6iOivqampsMGZ2aWu8MN/S1pyob0vDXVm4ETi9rVAptTvbZE3czMKuhwQ/9RYFpangY8UlSfIqmXpKEUPrBdnaaAdkg6PZ21M7Woj5mZVUiP9hpI+hnwFWCgpGZgHrAAWCrpcqAJuAggItZJWgq8ArQAMyOiNW1qBoUzgfoAT6aHmZlVULuhHxHfPMiq8QdpPx+YX6LeCIw6pNGZmVmH8hW5ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZqXjoS5oo6TVJGyTNrfT+zcxyVtHQl9Qd+DvgbGAE8E1JIyo5BjOznFX6SH8ssCEiNkbE+8ASYHKFx2Bmlq0eFd7fEODNotfNwBf2byRpOjA9vdwp6bUKjC0HA4G3qz2I9uhH1R6BVYl/PjvWn5UqVjr0VaIWBxQiFgGLOn84eZHUGBH11R6HWSn++ayMSk/vNAMnFr2uBTZXeAxmZtmqdOg/DwyTNFTSUcAU4NEKj8HMLFsVnd6JiBZJVwJPAd2BuyJiXSXHkDlPmdnHmX8+K0ARB0ypm5lZF+Urcs3MMuLQNzPLiEPfzCwjDn0zqwpJJ0nqlZa/ImmWpE9Ve1xdnUO/i5NUK+lhSdskbZG0TFJttcdlBiwDWiX9OXAnMBR4oLpD6voc+l3f3RSuhTiewm0wHks1s2rbExEtwDeAv42I/0Th59Q6kUO/66uJiLsjoiU97gFqqj0oM+ADSd8EpgGPp1rPKo4nCw79ru9tSZdJ6p4elwHbqz0oM+A7wBeB+RHxuqShwH1VHlOX54uzujhJnwFupfCfK4B/AmZHxBtVHZgZIKkP8JmI8J10K8Shb2ZVIenrwI3AURExVNJo4K8jYlKVh9alVfrWylYhkq5vY3VExN9UbDBmpTVQ+GKlZwAi4sU0xWOdyKHfdb1XotYXuBwYADj0rdpaIuJd6SNfs+Gph07m0O+iIuKmvcuSjgFmU/jgbAlw08H6mXU2ST8HZgIvS/oW0F3SMGAWhc+crBP57J0uTFJ/Sf8VWEvhF/xpEfG9iNha5aFZ3u6hcHv1TcAoYDeFi7LepXBwYp3IH+R2UZJuAC6gcI/yv4uInVUektk+kvoC1wMTgf/Oh9M6ERE3V21gGfD0Ttd1NYUjqO8D1xXNm4rCf6x+1RqYGfABhc+degFH47n8inHod1ER4ak7+1iSNBG4mcLtQU6LiD9VeUhZ8fSOmVWUpP8JXOGvSq0Oh76ZWUY8BWBmlhGHvplZRhz6ZmYZcehblyCpVdKLktZJ+mdJV0mq2s+3pE2SBh5m3/MljejoMZmBQ9+6jv8XEaMjYiTw74BzgHlVHtPhOh9w6FuncOhbl5NuMzEduFIF3SXdIOl5SWsl/QfY92Xcz6bvEH5F0u17/zqQ9DVJqyT9TtL/kHR0qm+S9MNUf0nSv071AZJ+KekFSX9P4SI40rrLJK1Of4n8vaTuqb5T0vz0l8lzkgZL+hIwCbghtT8pfWH4K2nsSyr6j2ldjkPfuqSI2Ejh53sQhTuLvhsRfwH8BfCXRbfwHUvh6uV/A5wEXJCmZb4PnBURpwGNwFVFm3871W8Drkm1ecDKiPg8hYuOPgMgaThwCXBGRIwGWoFLU5++wHMR8TngWeAvI+KfUv//nP5y+d/AXODzEXEqcEWH/SNZlnxFrnVle4+2vwacKunC9PpYYBjwPrA6/YJA0s+ALwO7KEyv/CbdvuIoYFXRdh9Kz2so3N8I4N/uXY6IJyT9MdXHA2OA59O2+gB7b3j3Ph9+N+waCtNSpawF7pf0D8A/lPnezUpy6FuXJOmzFI6qt1II//8YEU/t1+YrHHjPl0jtl0fENw+y+d3puZWP/h8qdaWjgMURcW2JdR/Eh1dH7r+tYudS+KUyCfiBpJER0XKQtmZt8vSOdTmSaoDbgVtTqD4FzJDUM60/Od3lEWCspKFpLv8SYCXwHHCGpD9P7f+VpJPb2e2zpGkbSWcDx6X6CuBCSYPSuv6S/qydbe0AjkntuwEnRsTTwF8Bn6JwgzKzw+Ijfesq+kh6EegJtFC4Xe/eW/TeAdQBv1NhjmUbhTNkoDBts4DCnP6zwMMRsUfSvwd+JqlXavd94H+1sf8fpva/A34NNAFExCuSvg/8MgX4BxS+QKStL6ZfAvxU0ixgCnCnpGMp/NXw44h4p4x/D7OSfO8dy1aa3rkmIs6r9ljMKsXTO2ZmGfGRvplZRnykb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWkf8PisKC6kdskTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 性别、是否老年人、是否有配偶、是否有家属等特征对客户流失的影响\n",
    "# 合并图\n",
    "baseCols = ['gender', 'SeniorCitizen', 'Partner', 'Dependents']\n",
    "\n",
    "for i in baseCols:\n",
    "    cnt = pd.crosstab(data[i], data['Churn'])    # 构建特征与目标变量的列联表\n",
    "    cnt.plot.bar(stacked = True)\n",
    "    plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 观察流失率与入网月数的关系\n",
    "# 折线图\n",
    "groupDf = data[['tenure', 'Churn']]    # 只需要用到两列数据\n",
    "groupDf['Churn'] = groupDf['Churn'].map({'Yes': 1, 'No': 0})    # 将正负样本目标变量改为1和0方便计算\n",
    "pctDf = groupDf.groupby(['tenure']).sum() / groupDf.groupby(['tenure']).count()    # 计算不同入网月数对应的流失率\n",
    "pctDf = pctDf.reset_index()    # 将索引变成列\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(pctDf['tenure'], pctDf['Churn'], label='Churn percentage')    # 绘制折线图\n",
    "plt.legend()    # 显示图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.619902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.516807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.471591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>0.084211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>0.092437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>0.035294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>0.016575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    tenure     Churn\n",
       "0        0  0.000000\n",
       "1        1  0.619902\n",
       "2        2  0.516807\n",
       "3        3  0.470000\n",
       "4        4  0.471591\n",
       "..     ...       ...\n",
       "68      68  0.090000\n",
       "69      69  0.084211\n",
       "70      70  0.092437\n",
       "71      71  0.035294\n",
       "72      72  0.016575\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pctDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 电话业务\n",
    "posDf = data[data['PhoneService'] == 'Yes']\n",
    "negDf = data[data['PhoneService'] == 'No']\n",
    "\n",
    "fig = plt.figure(figsize=(10,4)) # 建立图像\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "p1 = posDf['Churn'].value_counts()\n",
    "ax1.pie(p1,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax1.set_title('Churn of (PhoneService = Yes)')\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "p2 = negDf['Churn'].value_counts()\n",
    "ax2.pie(p2,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax2.set_title('Churn of (PhoneService = No)')\n",
    "\n",
    "plt.tight_layout(pad=0.5)    # 设置子图之间的间距\n",
    "plt.show() # 展示饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多线业务\n",
    "df1 = data[data['MultipleLines'] == 'Yes']\n",
    "df2 = data[data['MultipleLines'] == 'No']\n",
    "df3 = data[data['MultipleLines'] == 'No phone service']\n",
    "\n",
    "fig = plt.figure(figsize=(15,4)) # 建立图像\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "p1 = df1['Churn'].value_counts()\n",
    "ax1.pie(p1,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax1.set_title('Churn of (MultipleLines = Yes)')\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "p2 = df2['Churn'].value_counts()\n",
    "ax2.pie(p2,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax2.set_title('Churn of (MultipleLines = No)')\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "p3 = df3['Churn'].value_counts()\n",
    "ax3.pie(p3,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax3.set_title('Churn of (MultipleLines = No phone service)')\n",
    "\n",
    "plt.tight_layout(pad=0.5)    # 设置子图之间的间距\n",
    "plt.show() # 展示饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 互联网业务\n",
    "cnt = pd.crosstab(data['InternetService'], data['Churn'])    # 构建特征与目标变量的列联表\n",
    "cnt.plot.barh(stacked=True, figsize=(15,6))    # 绘制堆叠条形图，便于观察不同特征值流失的占比情况\n",
    "plt.show()    # 展示图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 与互联网相关的业务\n",
    "internetCols = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
    "\n",
    "for i in internetCols:\n",
    "    df1 = data[data[i] == 'Yes']\n",
    "    df2 = data[data[i] == 'No']\n",
    "    df3 = data[data[i] == 'No internet service']\n",
    "\n",
    "    fig = plt.figure(figsize=(10,3)) # 建立图像\n",
    "    plt.title(i)\n",
    "    \n",
    "    ax1 = fig.add_subplot(131)\n",
    "    p1 = df1['Churn'].value_counts()\n",
    "    ax1.pie(p1,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))    # 开通业务\n",
    "\n",
    "    ax2 = fig.add_subplot(132)\n",
    "    p2 = df2['Churn'].value_counts()\n",
    "    ax2.pie(p2,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))    # 未开通业务\n",
    "\n",
    "    ax3 = fig.add_subplot(133)\n",
    "    p3 = df3['Churn'].value_counts()\n",
    "    ax3.pie(p3,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))    # 未开通互联网业务\n",
    "  \n",
    "    plt.tight_layout()    # 设置子图之间的间距\n",
    "    plt.show() # 展示饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 合约期限\n",
    "df1 = data[data['Contract'] == 'Month-to-month']\n",
    "df2 = data[data['Contract'] == 'One year']\n",
    "df3 = data[data['Contract'] == 'Two year']\n",
    "\n",
    "fig = plt.figure(figsize=(15,4)) # 建立图像\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "p1 = df1['Churn'].value_counts()\n",
    "ax1.pie(p1,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax1.set_title('Churn of (Contract = Month-to-month)')\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "p2 = df2['Churn'].value_counts()\n",
    "ax2.pie(p2,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax2.set_title('Churn of (Contract = One year)')\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "p3 = df3['Churn'].value_counts()\n",
    "ax3.pie(p3,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax3.set_title('Churn of (Contract = Two year)')\n",
    "\n",
    "plt.tight_layout(pad=0.5)    # 设置子图之间的间距\n",
    "plt.show() # 展示饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 是否采用电子结算\n",
    "df1 = data[data['PaperlessBilling'] == 'Yes']\n",
    "df2 = data[data['PaperlessBilling'] == 'No']\n",
    "\n",
    "fig = plt.figure(figsize=(10,4)) # 建立图像\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "p1 = df1['Churn'].value_counts()\n",
    "ax1.pie(p1,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax1.set_title('Churn of (PaperlessBilling = Yes)')\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "p2 = df2['Churn'].value_counts()\n",
    "ax2.pie(p2,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax2.set_title('Churn of (PaperlessBilling = No)')\n",
    "\n",
    "plt.tight_layout(pad=0.5)    # 设置子图之间的间距\n",
    "plt.show() # 展示饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 付款方式\n",
    "df1 = data[data['PaymentMethod'] == 'Bank transfer (automatic)']    # 银行转账（自动）\n",
    "df2 = data[data['PaymentMethod'] == 'Credit card (automatic)']    # 信用卡（自动）\n",
    "df3 = data[data['PaymentMethod'] == 'Electronic check']    # 电子支票\n",
    "df4 = data[data['PaymentMethod'] == 'Mailed check']    # 邮寄支票\n",
    "\n",
    "fig = plt.figure(figsize=(10,8)) # 建立图像\n",
    "\n",
    "ax1 = fig.add_subplot(221)\n",
    "p1 = df1['Churn'].value_counts()\n",
    "ax1.pie(p1,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax1.set_title('Churn of (PaymentMethod = Bank transfer')\n",
    "\n",
    "ax2 = fig.add_subplot(222)\n",
    "p2 = df2['Churn'].value_counts()\n",
    "ax2.pie(p2,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax2.set_title('Churn of (PaymentMethod = Credit card)')\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "p3 = df3['Churn'].value_counts()\n",
    "ax3.pie(p3,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax3.set_title('Churn of (PaymentMethod = Electronic check)')\n",
    "\n",
    "ax4 = fig.add_subplot(224)\n",
    "p4 = df4['Churn'].value_counts()\n",
    "ax4.pie(p4,labels=['No','Yes'],autopct='%1.2f%%',explode=(0,0.1))\n",
    "ax4.set_title('Churn of (PaymentMethod = Mailed check)')\n",
    "\n",
    "plt.tight_layout(pad=0.5)    # 设置子图之间的间距\n",
    "plt.show() # 展示饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PaymentMethod\n",
       "Bank transfer (automatic)    1544\n",
       "Credit card (automatic)      1522\n",
       "Electronic check             2365\n",
       "Mailed check                 1612\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('PaymentMethod').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每月费用核密度估计图\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "negDf = data[data['Churn'] == 'No']\n",
    "sns.distplot(negDf['MonthlyCharges'], hist=False, label= 'No')\n",
    "posDf = data[data['Churn'] == 'Yes']\n",
    "sns.distplot(posDf['MonthlyCharges'], hist=False, label= 'Yes')\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每月费用核密度估计图\n",
    "plt.figure(figsize=(10, 5))    # 构建图像\n",
    "\n",
    "negDf = data[data['Churn'] == 'No']\n",
    "sns.distplot(negDf['TotalCharges'], hist=False, label= 'No')\n",
    "posDf = data[data['Churn'] == 'Yes']\n",
    "sns.distplot(posDf['TotalCharges'], hist=False, label= 'Yes')\n",
    "\n",
    "plt.show()    # 展示图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据分析结论\n",
    "\n",
    "三类特征：（已经排除id,target）\n",
    "\n",
    "\n",
    "\n",
    "无关特征：\n",
    "\n",
    "业务影响：\n",
    "\n",
    "\n",
    "月费统计：\n",
    "\n",
    "总费统计：（客户终生价值）\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征工程 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.277445</td>\n",
       "      <td>-1.160323</td>\n",
       "      <td>-0.992667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.066327</td>\n",
       "      <td>-0.259629</td>\n",
       "      <td>-0.172198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.236724</td>\n",
       "      <td>-0.362660</td>\n",
       "      <td>-0.958122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.514251</td>\n",
       "      <td>-0.746535</td>\n",
       "      <td>-0.193706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.236724</td>\n",
       "      <td>0.197365</td>\n",
       "      <td>-0.938930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tenure  MonthlyCharges  TotalCharges\n",
       "0 -1.277445       -1.160323     -0.992667\n",
       "1  0.066327       -0.259629     -0.172198\n",
       "2 -1.236724       -0.362660     -0.958122\n",
       "3  0.514251       -0.746535     -0.193706\n",
       "4 -1.236724        0.197365     -0.938930"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 数值特征标准化\n",
    "from sklearn.preprocessing import StandardScaler    # 导入标准化库\n",
    "\n",
    "#输入是二维ndarray 所以两个方括号\n",
    "\n",
    "scaler = StandardScaler()\n",
    "data[['tenure']] = scaler.fit_transform(data[['tenure']])\n",
    "data[['MonthlyCharges']] = scaler.fit_transform(data[['MonthlyCharges']])\n",
    "data[['TotalCharges']] = scaler.fit_transform(data[['TotalCharges']])\n",
    "\n",
    "data[['tenure', 'MonthlyCharges', 'TotalCharges']].head()    # 观察此时的数值特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 1)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['tenure']].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultipleLines特征还有0条样本的值为 'No phone service'\n",
      "OnlineSecurity特征还有0条样本的值为 'No internet service'\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "### 类别特征编码\n",
    "# 首先将部分特征值进行合并\n",
    "data.loc[data['MultipleLines']=='No phone service', 'MultipleLines'] = 'No'\n",
    "\n",
    "internetCols = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
    "for i in internetCols:\n",
    "    data.loc[data[i]=='No internet service', i] = 'No'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 部分类别特征只有两类取值，可以直接用0、1代替；另外，可视化过程中发现有四列特征对结果影响可以忽略，后续直接删除\n",
    "# 选择特征值为‘Yes’和 'No' 的列名\n",
    "encodeCols = list(data.columns[3: 17].drop(['tenure', 'PhoneService', 'InternetService', 'StreamingTV', 'StreamingMovies', 'Contract']))     \n",
    "for i in encodeCols:\n",
    "    data[i] = data[i].map({'Yes': 1, 'No': 0})    # 用1代替'Yes’，0代替 'No'\n",
    "# 顺便把目标变量也进行编码    \n",
    "data['Churn'] = data['Churn'].map({'Yes': 1, 'No': 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-hot Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehotCols = ['InternetService', 'Contract', 'PaymentMethod']\n",
    "churnDf = data['Churn'].to_frame()    # 取出目标变量列，以便后续进行合并\n",
    "featureDf = data.drop(['Churn'], axis=1)    # 所有特征列\n",
    "\n",
    "for i in onehotCols:\n",
    "    onehotDf = pd.get_dummies(featureDf[i],prefix=i) \n",
    "    featureDf = pd.concat([featureDf, onehotDf],axis=1)    # 编码后特征拼接到去除目标变量的数据集中\n",
    "\n",
    "data = pd.concat([featureDf, churnDf],axis=1)    # 拼回目标变量，确保目标变量在最后一列\n",
    "data = data.drop(onehotCols, axis=1)    # 删除原特征列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 28)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'OnlineSecurity',\n",
       "       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',\n",
       "       'StreamingMovies', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges',\n",
       "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
       "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
       "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check',\n",
       "       'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['customerID', 'gender', 'PhoneService', 'StreamingTV', 'StreamingMovies'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据数据分析的图，删除无效特征\n",
    "\n",
    "id\n",
    "\n",
    "性别，电话服务，电视服务，电影服务\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.066327</td>\n",
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       "      <td>-0.259629</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>-1.236724</td>\n",
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       "      <td>-0.362660</td>\n",
       "      <td>-0.958122</td>\n",
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       "      <td>0.514251</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen  Partner  Dependents    tenure  MultipleLines  \\\n",
       "0              0        1           0 -1.277445              0   \n",
       "1              0        0           0  0.066327              0   \n",
       "2              0        0           0 -1.236724              0   \n",
       "3              0        0           0  0.514251              0   \n",
       "4              0        0           0 -1.236724              0   \n",
       "\n",
       "   OnlineSecurity  OnlineBackup  DeviceProtection  TechSupport  \\\n",
       "0               0             1                 0            0   \n",
       "1               1             0                 1            0   \n",
       "2               1             1                 0            0   \n",
       "3               1             0                 1            1   \n",
       "4               0             0                 0            0   \n",
       "\n",
       "   PaperlessBilling  MonthlyCharges  TotalCharges  InternetService_DSL  \\\n",
       "0                 1       -1.160323     -0.992667                    1   \n",
       "1                 0       -0.259629     -0.172198                    1   \n",
       "2                 1       -0.362660     -0.958122                    1   \n",
       "3                 0       -0.746535     -0.193706                    1   \n",
       "4                 1        0.197365     -0.938930                    0   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  Contract_Month-to-month  \\\n",
       "0                            0                   0                        1   \n",
       "1                            0                   0                        0   \n",
       "2                            0                   0                        1   \n",
       "3                            0                   0                        0   \n",
       "4                            1                   0                        1   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0                  0                  0   \n",
       "1                  1                  0   \n",
       "2                  0                  0   \n",
       "3                  1                  0   \n",
       "4                  0                  0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        1   \n",
       "4                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  Churn  \n",
       "0                           0      0  \n",
       "1                           1      0  \n",
       "2                           1      1  \n",
       "3                           0      0  \n",
       "4                           0      1  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 23)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nu_fea = data[['tenure', 'MonthlyCharges', 'TotalCharges']]    # 选择连续型数值特征计算相关系数\n",
    "nu_fea = list(nu_fea)    # 特征名列表\n",
    "pearson_mat = data[nu_fea].corr(method='spearman')    # 计算皮尔逊相关系数矩阵\n",
    "\n",
    "plt.figure(figsize=(8,8)) # 建立图像\n",
    "sns.heatmap(pearson_mat, square=True, annot=True, cmap=\"YlGnBu\")    # 用热度图表示相关系数矩阵\n",
    "plt.show() # 展示热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['TotalCharges'], axis = 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 22)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 升采样 SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 此方法为网上抄的，改了一点为贴合本数据集合\n",
    "class Smote:\n",
    "    def __init__(self,samples,N=10,k=5):\n",
    "        self.n_samples,self.n_attrs=samples.shape\n",
    "        self.N=N\n",
    "        self.k=k\n",
    "        self.samples=samples\n",
    "        self.newindex=0\n",
    "        #self.synthetic=np.zeros((self.n_samples*N,self.n_attrs))\n",
    "\n",
    "    def over_sampling(self):\n",
    "        N=int(self.N)\n",
    "        self.synthetic = np.zeros((self.n_samples * N, self.n_attrs))\n",
    "        neighbors=NearestNeighbors(n_neighbors=self.k).fit(self.samples)\n",
    "        #print 'neighbors',neighbors\n",
    "        for i in range(len(self.samples)):\n",
    "            nnarray=neighbors.kneighbors(self.samples[i].reshape(1,-1),return_distance=False)[0]\n",
    "            #print nnarray\n",
    "            self._populate(N,i,nnarray)\n",
    "        return self.synthetic\n",
    "\n",
    "\n",
    "    # for each minority class samples,choose N of the k nearest neighbors and generate N synthetic samples.\n",
    "    def _populate(self,N,i,nnarray):\n",
    "        for j in range(N):\n",
    "            nn=random.randint(0,self.k-1)\n",
    "            dif=self.samples[nnarray[nn]]-self.samples[i]\n",
    "            gap=random.random()\n",
    "            self.synthetic[self.newindex]=self.samples[i]+gap*dif\n",
    "            self.newindex+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.00000</td>\n",
       "      <td>-1.236724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.362660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.245823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.198479</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.268652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.201274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.920378</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.187469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.995935</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.160295</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.036828</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.334218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.70955</td>\n",
       "      <td>-0.091315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.316561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.648794</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.379732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.215318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.237611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1        2         3    4         5    6    7    8    9         10  \\\n",
       "0  0.0  0.0  0.00000 -1.236724  0.0  0.069101  1.0  0.0  0.0  1.0 -0.494152   \n",
       "1  0.0  0.0  0.00000 -1.236724  0.0  1.000000  1.0  0.0  0.0  1.0 -0.362660   \n",
       "2  0.0  0.0  0.00000 -1.245823  0.0  0.000000  0.0  0.0  0.0  1.0  0.198479   \n",
       "3  0.0  0.0  0.00000 -1.268652  0.0  0.000000  0.0  0.0  0.0  1.0  0.201274   \n",
       "4  0.0  0.0  0.00000 -0.920378  1.0  0.000000  0.0  1.0  0.0  1.0  1.187469   \n",
       "5  0.0  0.0  0.00000 -0.995935  1.0  0.000000  0.0  1.0  0.0  1.0  1.160295   \n",
       "6  0.0  1.0  0.00000  0.036828  1.0  0.000000  0.0  1.0  1.0  1.0  1.334218   \n",
       "7  0.0  1.0  0.70955 -0.091315  1.0  0.000000  0.0  1.0  1.0  1.0  1.316561   \n",
       "8  0.0  0.0  0.00000  0.648794  1.0  0.000000  1.0  1.0  0.0  1.0  1.379732   \n",
       "9  0.0  0.0  0.00000  0.215318  1.0  0.000000  1.0  1.0  0.0  1.0  1.237611   \n",
       "\n",
       "    11   12   13   14   15   16   17   18   19   20  \n",
       "0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "1  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "2  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "3  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "4  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "5  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "6  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "7  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "8  0.0  1.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "9  0.0  1.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个正样本用SMOTE方法随机生成两个新的样本\n",
    "posDf = data[data['Churn'] == 1].drop(['Churn'], axis=1)    # 共1869条正样本, 取其所有特征列\n",
    "posArray = posDf.values    # pd.DataFrame -> np.array, 以满足SMOTE方法的输入要求\n",
    "newPosArray = Smote(posArray, 2, 5).over_sampling()\n",
    "newPosDf = pd.DataFrame(newPosArray)    # np.array -> pd.DataFrame\n",
    "\n",
    "newPosDf.head(10)    # 观察此时的新样本   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
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       "      <th>MonthlyCharges</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
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       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.236724</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.362660</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.245823</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.198479</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.268652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.201274</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.920378</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.187469</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.995935</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.160295</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036828</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.334218</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.091315</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.316561</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.648794</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.379732</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.215318</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.237611</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen  Partner  Dependents    tenure  MultipleLines  \\\n",
       "0              0        0           0 -1.236724              0   \n",
       "1              0        0           0 -1.236724              0   \n",
       "2              0        0           0 -1.245823              0   \n",
       "3              0        0           0 -1.268652              0   \n",
       "4              0        0           0 -0.920378              1   \n",
       "5              0        0           0 -0.995935              1   \n",
       "6              0        1           0  0.036828              1   \n",
       "7              0        1           1 -0.091315              1   \n",
       "8              0        0           0  0.648794              1   \n",
       "9              0        0           0  0.215318              1   \n",
       "\n",
       "   OnlineSecurity  OnlineBackup  DeviceProtection  TechSupport  \\\n",
       "0               0             1                 0            0   \n",
       "1               1             1                 0            0   \n",
       "2               0             0                 0            0   \n",
       "3               0             0                 0            0   \n",
       "4               0             0                 1            0   \n",
       "5               0             0                 1            0   \n",
       "6               0             0                 1            1   \n",
       "7               0             0                 1            1   \n",
       "8               0             1                 1            0   \n",
       "9               0             1                 1            0   \n",
       "\n",
       "   PaperlessBilling  MonthlyCharges  InternetService_DSL  \\\n",
       "0                 1       -0.494152                    1   \n",
       "1                 1       -0.362660                    1   \n",
       "2                 1        0.198479                    0   \n",
       "3                 1        0.201274                    0   \n",
       "4                 1        1.187469                    0   \n",
       "5                 1        1.160295                    0   \n",
       "6                 1        1.334218                    0   \n",
       "7                 1        1.316561                    0   \n",
       "8                 1        1.379732                    0   \n",
       "9                 1        1.237611                    0   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  Contract_Month-to-month  \\\n",
       "0                            0                   0                        1   \n",
       "1                            0                   0                        1   \n",
       "2                            1                   0                        1   \n",
       "3                            1                   0                        1   \n",
       "4                            1                   0                        1   \n",
       "5                            1                   0                        1   \n",
       "6                            1                   0                        1   \n",
       "7                            1                   0                        1   \n",
       "8                            1                   0                        1   \n",
       "9                            1                   0                        1   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0                  0                  0   \n",
       "1                  0                  0   \n",
       "2                  0                  0   \n",
       "3                  0                  0   \n",
       "4                  0                  0   \n",
       "5                  0                  0   \n",
       "6                  0                  0   \n",
       "7                  0                  0   \n",
       "8                  0                  0   \n",
       "9                  0                  0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "7                                        0   \n",
       "8                                        1   \n",
       "9                                        1   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               0   \n",
       "1                                      0                               0   \n",
       "2                                      0                               1   \n",
       "3                                      0                               1   \n",
       "4                                      0                               1   \n",
       "5                                      0                               1   \n",
       "6                                      0                               1   \n",
       "7                                      0                               1   \n",
       "8                                      0                               0   \n",
       "9                                      0                               0   \n",
       "\n",
       "   PaymentMethod_Mailed check  Churn  \n",
       "0                           1      1  \n",
       "1                           1      1  \n",
       "2                           0      1  \n",
       "3                           0      1  \n",
       "4                           0      1  \n",
       "5                           0      1  \n",
       "6                           0      1  \n",
       "7                           0      1  \n",
       "8                           0      1  \n",
       "9                           0      1  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newPosDf.columns = posDf.columns    # 还原特征名\n",
    "cateCols = list(newPosDf.columns.drop(['tenure', 'MonthlyCharges']))   # 提取离散特征名组成的列表\n",
    "for i in cateCols:\n",
    "    newPosDf[i] = newPosDf[i].apply(lambda x: 1 if x >= 0.5 else 0)    # 将特征值变回0、1二元数值\n",
    "newPosDf['Churn'] = 1    # 添加目标变量列\n",
    "\n",
    "newPosDf.head(10)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3738, 22)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newPosDf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1869, 21)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posDf.shape # 我们裁 3305 个 pos 使得正负样例等数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "newPosDf = newPosDf[:3305]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data, newPosDf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10348, 22)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "def kFold_cv(X, y, classifier, **kwargs):\n",
    "    \"\"\"\n",
    "    :param X: 特征\n",
    "    :param y: 目标变量\n",
    "    :param classifier: 分类器\n",
    "    :param **kwargs: 参数\n",
    "    :return: 预测结果\n",
    "    \"\"\"\n",
    "    kf = KFold(n_splits=5, shuffle=True) \n",
    "    y_pred = np.zeros(len(y))    # 初始化y_pred数组\n",
    "    \n",
    "    for train_index, test_index in kf.split(X):  \n",
    "        X_train = X[train_index]    \n",
    "        X_test = X[test_index]\n",
    "        y_train = y[train_index]    # 划分数据集\n",
    "        clf = classifier(**kwargs)    \n",
    "        clf.fit(X_train, y_train)    # 模型训练\n",
    "        y_pred[test_index] = clf.predict(X_test)    # 模型预测\n",
    "    \n",
    "    return y_pred "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:54:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[11:54:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[11:54:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[11:54:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[11:54:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR    \n",
    "from sklearn.svm import SVC    \n",
    "from sklearn.ensemble import RandomForestClassifier as RF    \n",
    "from sklearn.ensemble import AdaBoostClassifier as Adaboost \n",
    "from xgboost import XGBClassifier as XGB    \n",
    "\n",
    "# X = data.iloc[:, :-1].as_matrix()\n",
    "X = data.iloc[:, :-1].iloc[:,:].values # Kagging\n",
    "y = data.iloc[:, -1].values\n",
    "\n",
    "# 没调参数\n",
    "lr_pred = kFold_cv(X, y, LR)\n",
    "svc_pred = kFold_cv(X, y, SVC)\n",
    "rf_pred = kFold_cv(X, y, RF)\n",
    "ada_pred = kFold_cv(X, y, Adaboost)\n",
    "xgb_pred = kFold_cv(X, y, XGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LR</th>\n",
       "      <th>SVC</th>\n",
       "      <th>RandomForest</th>\n",
       "      <th>AdaBoost</th>\n",
       "      <th>XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.798222</td>\n",
       "      <td>0.805180</td>\n",
       "      <td>0.908775</td>\n",
       "      <td>0.820642</td>\n",
       "      <td>0.851372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.749274</td>\n",
       "      <td>0.762585</td>\n",
       "      <td>0.827963</td>\n",
       "      <td>0.749515</td>\n",
       "      <td>0.812281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-score</th>\n",
       "      <td>0.772974</td>\n",
       "      <td>0.783304</td>\n",
       "      <td>0.866489</td>\n",
       "      <td>0.783467</td>\n",
       "      <td>0.831367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LR       SVC  RandomForest  AdaBoost   XGBoost\n",
       "Recall     0.798222  0.805180      0.908775  0.820642  0.851372\n",
       "Precision  0.749274  0.762585      0.827963  0.749515  0.812281\n",
       "F1-score   0.772974  0.783304      0.866489  0.783467  0.831367"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score    # 导入精确率、召回率、F1值等评价指标\n",
    "\n",
    "scoreDf = pd.DataFrame(columns=['LR', 'SVC', 'RandomForest', 'AdaBoost', 'XGBoost'])\n",
    "pred = [lr_pred, svc_pred, rf_pred, ada_pred, xgb_pred]\n",
    "for i in range(5):\n",
    "    r = recall_score(y, pred[i])\n",
    "    p = precision_score(y, pred[i])\n",
    "    f1 = f1_score(y, pred[i])\n",
    "    scoreDf.iloc[:, i] = pd.Series([r, p, f1])\n",
    "\n",
    "scoreDf.index = ['Recall', 'Precision', 'F1-score']\n",
    "scoreDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.247265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.241953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <td>0.086370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Two year</th>\n",
       "      <td>0.047713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <td>0.040761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <td>0.030489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.028156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>0.026900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>0.026011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>0.024770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>0.022996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>0.022744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>0.022658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>0.022227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_No</th>\n",
       "      <td>0.021774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0.020235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_One year</th>\n",
       "      <td>0.018491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <td>0.012957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <td>0.012539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <td>0.012373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <td>0.010616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         importance\n",
       "MonthlyCharges                             0.247265\n",
       "tenure                                     0.241953\n",
       "Contract_Month-to-month                    0.086370\n",
       "Contract_Two year                          0.047713\n",
       "InternetService_Fiber optic                0.040761\n",
       "PaymentMethod_Electronic check             0.030489\n",
       "PaperlessBilling                           0.028156\n",
       "OnlineSecurity                             0.026900\n",
       "Partner                                    0.026011\n",
       "TechSupport                                0.024770\n",
       "OnlineBackup                               0.022996\n",
       "MultipleLines                              0.022744\n",
       "Dependents                                 0.022658\n",
       "DeviceProtection                           0.022227\n",
       "InternetService_No                         0.021774\n",
       "SeniorCitizen                              0.020235\n",
       "Contract_One year                          0.018491\n",
       "PaymentMethod_Credit card (automatic)      0.012957\n",
       "PaymentMethod_Bank transfer (automatic)    0.012539\n",
       "InternetService_DSL                        0.012373\n",
       "PaymentMethod_Mailed check                 0.010616"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征重要性\n",
    "X = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, -1].values\n",
    "\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=0)\n",
    "y_pred = np.zeros(len(y))    # 初始化y_pred数组\n",
    "clf = RF()\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "    X_train = X[train_index]    \n",
    "    X_test = X[test_index]\n",
    "    y_train = y[train_index]    # 划分数据集\n",
    "    clf.fit(X_train, y_train)    # 模型训练\n",
    "    y_pred[test_index] = clf.predict(X_test)    # 模型预测\n",
    "    \n",
    "feature_importances = pd.DataFrame(clf.feature_importances_,\n",
    "                                   index = data.columns.drop(['Churn']),\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "feature_importances    # 查看特征重要性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 决策"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对老人推出优惠\n",
    "# 对未婚、无家属的客户推出优惠\n",
    "# 对新入网用户提供一定时期的优惠活动，直至客户到达稳定期\n",
    "# 提高电话服务、光纤网络、网络电视、网络电影等的客户体验，尝试提高用户的留存率，避免客户流失\n",
    "# 对能够帮助客户留存的在线安全、在线备份、设备保护、技术支持等互联网增值业务，加大宣传推广力度\n",
    "# 逐月付费用户推出年费优惠活动\n",
    "# 使用电子结算、电子支票的客户，推出其他支付方式的优惠活动\n",
    "# 每月费用在70～110(高消费)之间推出一定的优惠活动"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_cv(X, y, classifier, **kwargs):\n",
    "    \"\"\"\n",
    "    :param X: 特征\n",
    "    :param y: 目标变量\n",
    "    :param classifier: 分类器\n",
    "    :param **kwargs: 参数\n",
    "    :return: 预测结果\n",
    "    \"\"\"\n",
    "    kf = KFold(n_splits=5, random_state=0)\n",
    "    y_pred = np.zeros(len(y))    \n",
    "    \n",
    "    for train_index, test_index in kf.split(X):\n",
    "        X_train = X[train_index]    \n",
    "        X_test = X[test_index]\n",
    "        y_train = y[train_index]    \n",
    "        clf = classifier(**kwargs)    \n",
    "        clf.fit(X_train, y_train)    \n",
    "        y_pred[test_index] = clf.predict_proba(X_test)[:,1]    # 注：此处预测的是概率值\n",
    "    \n",
    "    return y_pred  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob = prob_cv(X, y, RF)    # 预测概率值\n",
    "prob = np.round(prob, 1)    # 对预测出的概率值保留一位小数，便于分组观察\n",
    "\n",
    "# 合并预测值和真实值\n",
    "probDf = pd.DataFrame(prob)\n",
    "churnDf = pd.DataFrame(y)\n",
    "df1 = pd.concat([probDf, churnDf], axis=1)\n",
    "df1.columns = ['prob', 'churn']\n",
    "\n",
    "df1 = df1[:7043]    # 只取原始数据集的7043条样本进行决策\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group = df1.groupby(['prob'])\n",
    "cnt = group.count()    # 每种概率值对应的样本数\n",
    "true_prob = group.sum() / group.count()    # 真实流失率\n",
    "df2 = pd.concat([cnt,true_prob], axis=1).reset_index()\n",
    "df2.columns = ['prob', 'cnt', 'true_prob']\n",
    "\n",
    "df2"
   ]
  }
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